Sustainable design of self-consolidating green concrete with partial replacements for cement through neural-network and fuzzy technique

In order to achieve a sustainable mix design, this paper evaluates self-consolidating green concrete (SCGC) properties by experimental tests and then examines the design parameters with an artificial intelligence technique. In this regard, cement was partially replaced in different contents with granulated blast furnace slag (GBFS) powder, volcanic powder, fly ash, and micro-silica. Moreover, fresh and hardened properties tests were performed on the specimens. Finally, an adaptive neuro-fuzzy inference system (ANFIS) was developed to identify the influencing parameters on the compressive strength of the specimens. For this purpose, seven ANFIS models evaluated the input parameters separately, and in terms of optimization, twenty-one models were assigned to different combinations of inputs. Experimental results were reported and discussed completely, where furnace slag represented the most effect on the hardened properties in binary mixes, and volcanic powder played an effective role in slump retention among other cement replacements. However, the combination of micro-silica and volcanic powder as a ternary mix design successfully achieved the most improvement compared to other mix designs. Furthermore, ANFIS results showed that binder content has the highest governing parameters in terms of the strength of SCGC. Finally, when compared with other additive powders, the combination of micro-silica with volcanic powder provided the most strength, which has also been verified and reported by the test results

Development of an Ammonia Reduction After-Treatment Systems for Stoichiometric Natural Gas Engines

Three-way catalyst (TWC) equipped stoichiometric natural gas vehicles have proven to be an effective alternative fuel strategy that shows significant low NOx emissions characteristics. However, recent studies have shown the TWC activity to contribute to elevated levels of tailpipe ammonia (NH 3) emissions. Although a non-regulated pollutant, ammonia is a potent pre-cursor to ambient secondary PM formation. Ammonia is an inevitable byproduct of fuel rich operation that results in lowest NOx slip through the TWC after-treatment system.;The main objective of the study is to develop a passive Ammonia Reduction Catalyst (passive-ARC) based NH3 reduction strategy that results in an overall reduction of ammonia as well as NOx emissions. The study investigated the characteristics of Fe-based and Cu-based zeolites SCR catalysts in storage and desorption of ammonia at high exhaust temperature conditions, that are typical of stoichiometric natural gas engines. Continuous measurements of NOx and NH3 before and after the SCR systems were conducted using a Fourier Transform Infrared Spectrometry (FTIR) gas analyzer. Results of the investigation showed that both, the Fe- and Cu zeolite SCRs adsorbed above 90% of TWC generated NH3 emissions below 350--375 °C SCR temperatures. Desorption or slipping of NH3 was observed at exhaust gas temperatures exceeding 400 °C. In terms of NOx conversions, Fe-zeolite showed efficiency between 50--80% above temperatures of 300--350 °C while Cu-zeolite performed well at lower SCR temperature from 250 °C and above with a conversion efficiency of greater than 50%.;In order to efficiently reduce both NOx and NH3 simultaneously over longer durations it was found that an engine-based air fuel ratio operation strategy for the passive-ARC system must be developed. To this extent, the study extended its objectives to develop an engine-based control strategy that results in stoichiometric ammonia production operation followed by brief lean operation to regenerate the saturated ammonia reduction catalyst using high NOx slip through TWC. The study presents comprehensive results of ammonia storage characteristics of SCRs pertaining to stoichiometric natural gas engine exhaust as well as an advanced engine control strategy approach to simultaneously reduce both NOx and NH3 using an alternating air -fuel ratio approach

Fuzzy control and its application to a pH process

In the chemical industry, the control of pH is a well-known problem that presents difficulties due to the large variations in its process dynamics and the static nonlinearity between pH and concentration. pH control requires the application of advanced control techniques such as linear or nonlinear adaptive control methods. Unfortunately, adaptive controllers rely on a mathematical model of the process being controlled, the parameters being determined or modified in real time. Because of its characteristics, the pH control process is extremely difficult to model accurately. Fuzzy logic, which is derived from Zadeh's theory of fuzzy sets and algorithms, provides an effective means of capturing the approximate, inexact nature of the physical world. It can be used to convert a linguistic control strategy based on expert knowledge, into an automatic control strategy to control a system in the absence of an exact mathematical model. The work described in this thesis sets out to investigate the suitability of fuzzy techniques for the control of pH within a continuous flow titration process. Initially, a simple fuzzy development system was designed and used to produce an experimental fuzzy control program. A detailed study was then performed on the relationship between fuzzy decision table scaling factors and the control constants of a digital PI controller. Equation derived from this study were then confirmed experimentally using an analogue simulation of a first order plant. As a result of this work a novel method of tuning a fuzzy controller by adjusting its scaling factors, was derived. This technique was then used for the remainder of the work described in this thesis. The findings of the simulation studies were confirmed by an extensive series of experiments using a pH process pilot plant. The performance of the tunable fuzzy controller was compared with that of a conventional PI controller in response to step change in the set-point, at a number of pH levels. The results showed not only that the fuzzy controller could be easily adjusted to provided a wide range of operating characteristics, but also that the fuzzy controller was much better at controlling the highly non-linear pH process, than a conventional digital PI controller. The fuzzy controller achieved a shorter settling time, produced less over-shoot, and was less affected by contamination than the digital PI controller. One of the most important characteristics of the tunable fuzzy controller is its ability to implement a wide variety of control mechanisms simply by modifying one or two control variables. Thus the controller can be made to behave in a manner similar to that of a conventional PI controller, or with different parameter values, can imitate other forms of controller. One such mode of operation uses sliding mode control, with the fuzzy decision table main diagonal being used as the variable structure system (VSS) switching line. A theoretical explanation of this behavior, and its boundary conditions, are given within the text. While the work described within this thesis has concentrated on the use of fuzzy techniques in the control of continuous flow pH plants, the flexibility of the fuzzy control strategy described here, make it of interest in other areas. It is likely to be particularly useful in situations where high degrees of non-linearity make more conventional control methods ineffective

Fuzzy controllers design using space-filling curves

We present a clustering technique for fuzzy rules based on Hilbert space-filling curves (SFC). SFC scans an n-dimensional space and reduces it to a curve, i.e. a one-dimensional line. We first introduce the Hilbert space-filling curves, and outline the algorithms for clustering and adaptive clustering which demonstrate SFC efficient self-organizing features. We then propose a SFC fuzzy inference model based on clustering the object space. The SFC fuzzy model is then used to design a fuzzy controller. The proposed method achieves a dramatic reduction of the complexity of fuzzy controller by reducing the multivariable fuzzification problem to a one dimensional spac

Fuzzy Controllers Design Using Space-Filling Curves

In this paper we present a clustering technique for fuzzy rules based on Hilbert Space-filling Curves (SFC). SFC scans an n-dimensional space and reduces it to a curve, i.e. a one-dimensional line. The paper introduces first the Hilber Space-filling curves, and outlines algorithms for clustering and adaptive clustering which demonstrate the SFC efficient self-organizing features. We then propose a SFC fuzzy inference model based on clustering the object space. The SFC fuzzy model is then used to design a fuzzy controller. The proposed method achieves a dramatic reduction of the complexity of fuzzy controller by reducing the multivariable fuzzification problem to a one dimentional space

Fuzzy Object retrieval by using histogram of fuzzy Allen relations

Abstract-Relative position of object description are widely used in event understanding and computer vision tasks especially in object recognition. Use of low level features cannot give satisfactory results when high level concepts is not easily expressible in low level contents. Mostly researchers are concentrating on spatio-temporal relationship between objects or regions of an object in images. Object retrieval which is taken into account the relative position of objects in images become important. In such a case classical Allen relations are used. Searched object can take various shapes and scale according to shooting. Fuzzy methods have the ability to compensate the imprecise informations and vagueness. In this paper fuzzy histograms of Allen relations are used for object retrieval. Fuzzy histograms of Allen relations are the quantitative representation of relative object position. For this purpose Matsakis's [9] algorithm for fuzzification of line segments is refined. This representation is affine invariant. Query is made by example and only corresponding relative relation between objects is considered. Results are analyzed by a well known Receiver Operating Characteristic curve ( ROC )method

Fast and robust image feature matching methods for computer vision applications

Service robotic systems are designed to solve tasks such as recognizing and manipulating objects, understanding natural scenes, navigating in dynamic and populated environments. It's immediately evident that such tasks cannot be modeled in all necessary details as easy as it is with industrial robot tasks; therefore, service robotic system has to have the ability to sense and interact with the surrounding physical environment through a multitude of sensors and actuators. Environment sensing is one of the core problems that limit the deployment of mobile service robots since existing sensing systems are either too slow or too expensive. Visual sensing is the most promising way to provide a cost effective solution to the mobile robot sensing problem. It's usually achieved using one or several digital cameras placed on the robot or distributed in its environment. Digital cameras are information rich sensors and are relatively inexpensive and can be used to solve a number of key problems for robotics and other autonomous intelligent systems, such as visual servoing, robot navigation, object recognition, pose estimation, and much more. The key challenges to taking advantage of this powerful and inexpensive sensor is to come up with algorithms that can reliably and quickly extract and match the useful visual information necessary to automatically interpret the environment in real-time. Although considerable research has been conducted in recent years on the development of algorithms for computer and robot vision problems, there are still open research challenges in the context of the reliability, accuracy and processing time. Scale Invariant Feature Transform (SIFT) is one of the most widely used methods that has recently attracted much attention in the computer vision community due to the fact that SIFT features are highly distinctive, and invariant to scale, rotation and illumination changes. In addition, SIFT features are relatively easy to extract and to match against a large database of local features. Generally, there are two main drawbacks of SIFT algorithm, the first drawback is that the computational complexity of the algorithm increases rapidly with the number of key-points, especially at the matching step due to the high dimensionality of the SIFT feature descriptor. The other one is that the SIFT features are not robust to large viewpoint changes. These drawbacks limit the reasonable use of SIFT algorithm for robot vision applications since they require often real-time performance and dealing with large viewpoint changes. This dissertation proposes three new approaches to address the constraints faced when using SIFT features for robot vision applications, Speeded up SIFT feature matching, robust SIFT feature matching and the inclusion of the closed loop control structure into object recognition and pose estimation systems. The proposed methods are implemented and tested on the FRIEND II/III service robotic system. The achieved results are valuable to adapt SIFT algorithm to the robot vision applications